1. Field of the Invention
The present invention relates to an object identification apparatus and a method for identifying an object in image data.
2. Description of the Related Art
A face identification technique, for example, for identifying an individual face has been known as an identification technique for identifying an object in image data with an object in another image. Hereinafter, in the present specification, the term “identification of an object” refers to the determination of difference in an object as an individual (difference in person as an individual, for example). On the other hand, the term “detection of an object” refers to the determination of an individual falling under the same category without discriminating individuals (for example, a face is detected without discriminating individuals).
A method described in the following literature, for example, is known as the face identification technique, “Baback Moghaddam, Beyond Eigenfaces: Probabilistic Matching for Face Recognition (M.I.T. Media Laboratory Perceptual Computing Section Technical Report No. 433), Probabilistic Visual Learning For Object Representation (IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, July 1997).” This method is algorithm that enables the registration and the additional learning of a face in real time by replacing an issue of identifying an individual by a face with a two-class identification issue of a feature class called a difference face.
The face identification using a support vector machine (SVM), for example, requires n SVM identifiers for identifying registered human faces from other faces to identify n human faces. Learning of the SVM is needed when registering human faces. The learning by the SVM requires a large amount of data of human faces desired to be registered, already registered human faces and other human faces and is time-consuming, so that a previously calculating method is generally used.
According to the methods described in the above literature, the need for the additional learning can be eliminated by replacing the issue of identifying an individual with identification issues of two classes described below:
Intra-personal class: Feature class such as variation in illumination and variation in expression and orientation between the images of the same person; and
Extra-personal class: Variation feature class between the images of a different person.
Assuming that the distribution of the above two classes is constant irrespective of a specific individual, an individual face identification issue is caused to result in identification issues of the above two classes to form an identifier. A large amount of images is previously prepared to learn an identifier for identifying the variation feature class between the same persons from the variation feature class between different persons.
For a new register, only the image of its face (or, a result in which a required feature is extracted) may be stored. In identification, a difference feature is drawn out from two images to cause the identifier to determine whether a person is the same person or not. This eliminates the need for learning the SVM to enable registration at real time.
Factors lowering the identification performance of an apparatus and a method for identifying objects (more specifically, human faces) as described above arise from variation between two images for registration and authentication. In other words, the factors arise from variation between two images of objects (human faces) to be identified, more specifically, those arise from occlusion due to illumination condition, direction, orientation, and other objects and variation due to expression. Increase in such a variation significantly reduces the identification performance.
In Japanese Patent Application Laid-Open No. 2003-323622, the above problem is solved in such a manner that a pattern matching is carried out several times for each partial area, outliers among the results are removed, and matching degrees of each partial area are integrated to ensure robustness to the variation.
The feature quantity per se is desirably robust to variation to maintain the identification performance even under the condition that variation is great like a human face and various image shooting conditions. Alternatively, an approach is effective in which such a conversion that an extracted feature quantity is made robust to variation is provided to improve the identification performance.
In application to a digital camera or a web camera, it is general that shooting condition for and variation (direction and expression) in an image are greatly different between in registration and in identification. The selection of the feature quantity that is robust to variation and a method of changing the feature quantity is a significant problem of improving an identification rate.